from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-22 14:11:21.303100
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 22, Dec, 2020
Time: 14:11:25
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.9424
Nobs: 148.000 HQIC: -45.0245
Log likelihood: 1586.58 FPE: 1.33452e-20
AIC: -45.7650 Det(Omega_mle): 7.40912e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.471227 0.165613 2.845 0.004
L1.Burgenland 0.143070 0.083071 1.722 0.085
L1.Kärnten -0.238712 0.066962 -3.565 0.000
L1.Niederösterreich 0.088645 0.198378 0.447 0.655
L1.Oberösterreich 0.255428 0.165238 1.546 0.122
L1.Salzburg 0.175190 0.085822 2.041 0.041
L1.Steiermark 0.084715 0.120020 0.706 0.480
L1.Tirol 0.150575 0.079035 1.905 0.057
L1.Vorarlberg 0.006264 0.077115 0.081 0.935
L1.Wien -0.118182 0.161701 -0.731 0.465
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.517424 0.216393 2.391 0.017
L1.Burgenland 0.009436 0.108543 0.087 0.931
L1.Kärnten 0.364068 0.087494 4.161 0.000
L1.Niederösterreich 0.123026 0.259205 0.475 0.635
L1.Oberösterreich -0.185976 0.215903 -0.861 0.389
L1.Salzburg 0.199679 0.112137 1.781 0.075
L1.Steiermark 0.245138 0.156820 1.563 0.118
L1.Tirol 0.141340 0.103269 1.369 0.171
L1.Vorarlberg 0.187402 0.100760 1.860 0.063
L1.Wien -0.584316 0.211281 -2.766 0.006
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.283410 0.071860 3.944 0.000
L1.Burgenland 0.104979 0.036045 2.912 0.004
L1.Kärnten -0.022040 0.029055 -0.759 0.448
L1.Niederösterreich 0.089205 0.086077 1.036 0.300
L1.Oberösterreich 0.288946 0.071698 4.030 0.000
L1.Salzburg 0.001964 0.037239 0.053 0.958
L1.Steiermark -0.023562 0.052077 -0.452 0.651
L1.Tirol 0.086277 0.034294 2.516 0.012
L1.Vorarlberg 0.131467 0.033461 3.929 0.000
L1.Wien 0.065467 0.070163 0.933 0.351
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.185930 0.083161 2.236 0.025
L1.Burgenland -0.008882 0.041713 -0.213 0.831
L1.Kärnten 0.021081 0.033625 0.627 0.531
L1.Niederösterreich 0.009470 0.099614 0.095 0.924
L1.Oberösterreich 0.415079 0.082973 5.003 0.000
L1.Salzburg 0.097702 0.043095 2.267 0.023
L1.Steiermark 0.194937 0.060267 3.235 0.001
L1.Tirol 0.031313 0.039687 0.789 0.430
L1.Vorarlberg 0.102188 0.038723 2.639 0.008
L1.Wien -0.050559 0.081196 -0.623 0.533
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.566825 0.174862 3.242 0.001
L1.Burgenland 0.074504 0.087711 0.849 0.396
L1.Kärnten 0.007296 0.070702 0.103 0.918
L1.Niederösterreich -0.063113 0.209457 -0.301 0.763
L1.Oberösterreich 0.157094 0.174466 0.900 0.368
L1.Salzburg 0.049585 0.090615 0.547 0.584
L1.Steiermark 0.130305 0.126722 1.028 0.304
L1.Tirol 0.214072 0.083449 2.565 0.010
L1.Vorarlberg 0.016749 0.081422 0.206 0.837
L1.Wien -0.137877 0.170731 -0.808 0.419
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.175286 0.120870 1.450 0.147
L1.Burgenland -0.033452 0.060628 -0.552 0.581
L1.Kärnten -0.016701 0.048872 -0.342 0.733
L1.Niederösterreich 0.153311 0.144783 1.059 0.290
L1.Oberösterreich 0.411106 0.120597 3.409 0.001
L1.Salzburg -0.026433 0.062636 -0.422 0.673
L1.Steiermark -0.045708 0.087595 -0.522 0.602
L1.Tirol 0.191234 0.057683 3.315 0.001
L1.Vorarlberg 0.036248 0.056282 0.644 0.520
L1.Wien 0.165104 0.118015 1.399 0.162
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.203129 0.151858 1.338 0.181
L1.Burgenland 0.078084 0.076172 1.025 0.305
L1.Kärnten -0.042428 0.061401 -0.691 0.490
L1.Niederösterreich -0.034746 0.181901 -0.191 0.849
L1.Oberösterreich -0.122266 0.151514 -0.807 0.420
L1.Salzburg 0.011645 0.078694 0.148 0.882
L1.Steiermark 0.392082 0.110051 3.563 0.000
L1.Tirol 0.516890 0.072471 7.132 0.000
L1.Vorarlberg 0.224456 0.070710 3.174 0.002
L1.Wien -0.225060 0.148270 -1.518 0.129
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.115856 0.175634 0.660 0.509
L1.Burgenland 0.029390 0.088098 0.334 0.739
L1.Kärnten -0.118281 0.071014 -1.666 0.096
L1.Niederösterreich 0.158354 0.210381 0.753 0.452
L1.Oberösterreich 0.020094 0.175236 0.115 0.909
L1.Salzburg 0.223745 0.091015 2.458 0.014
L1.Steiermark 0.148336 0.127282 1.165 0.244
L1.Tirol 0.091512 0.083817 1.092 0.275
L1.Vorarlberg 0.038086 0.081781 0.466 0.641
L1.Wien 0.304467 0.171485 1.775 0.076
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.584210 0.097637 5.983 0.000
L1.Burgenland -0.017145 0.048975 -0.350 0.726
L1.Kärnten -0.000833 0.039478 -0.021 0.983
L1.Niederösterreich -0.036549 0.116954 -0.313 0.755
L1.Oberösterreich 0.283860 0.097416 2.914 0.004
L1.Salzburg 0.009456 0.050596 0.187 0.852
L1.Steiermark 0.008542 0.070757 0.121 0.904
L1.Tirol 0.077298 0.046595 1.659 0.097
L1.Vorarlberg 0.180802 0.045463 3.977 0.000
L1.Wien -0.084066 0.095330 -0.882 0.378
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.131459 -0.016985 0.195447 0.237944 0.037431 0.088836 -0.117659 0.150664
Kärnten 0.131459 1.000000 -0.019903 0.182368 0.131578 -0.158275 0.166955 0.019561 0.295267
Niederösterreich -0.016985 -0.019903 1.000000 0.251002 0.072012 0.189845 0.085969 0.018673 0.348347
Oberösterreich 0.195447 0.182368 0.251002 1.000000 0.269676 0.276684 0.088485 0.054978 0.080292
Salzburg 0.237944 0.131578 0.072012 0.269676 1.000000 0.138832 0.059798 0.065925 -0.039956
Steiermark 0.037431 -0.158275 0.189845 0.276684 0.138832 1.000000 0.092073 0.069492 -0.163334
Tirol 0.088836 0.166955 0.085969 0.088485 0.059798 0.092073 1.000000 0.130830 0.116904
Vorarlberg -0.117659 0.019561 0.018673 0.054978 0.065925 0.069492 0.130830 1.000000 0.077070
Wien 0.150664 0.295267 0.348347 0.080292 -0.039956 -0.163334 0.116904 0.077070 1.000000